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      Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index

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          FSIM: a feature similarity index for image quality assessment.

          Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.
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            Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures

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              Multiscale structural similarity for image quality assessment

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                Author and article information

                Journal
                IEEE Transactions on Image Processing
                IEEE Trans. on Image Process.
                Institute of Electrical and Electronics Engineers (IEEE)
                1057-7149
                1941-0042
                February 2014
                February 2014
                : 23
                : 2
                : 684-695
                Article
                10.1109/TIP.2013.2293423
                26270911
                ebf402cb-f476-4682-80f9-34d71a499818
                © 2014
                History

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